Secure multi-party computation: information flow of outputs and game theory
File(s)
Accepted version
Author(s)
Ah-Fat, P
Huth, MRA
Type
Conference Paper
Abstract
Secure multiparty computation enables protocol participants
to compute the output of a public function of their private inputs whilst
protecting the confidentiality of their inputs. But such an output, as a
function of its inputs, inevitably leaks some information about input val-
ues regardless of the protocol used to compute it. We introduce founda-
tions for quantifying and understanding how such leakage may influence
input behaviour of deceitful protocol participants as well as that of par-
ticipants they target. Our model captures the beliefs and knowledge that
participants have about what input values other participants may choose.
In this model, measures of information flow that may arise between pro-
tocol participants are introduced, formally investigated, and experimen-
tally evaluated. These information-theoretic measures not only suggest
advantageous input behaviour to deceitful participants for optimal up-
dates of their beliefs about chosen inputs of targeted participants. They
also allow targets to quantify the information-flow risk of their input
choices. We show that this approach supports a game-theoretic formula-
tion in which deceitful attackers wish to maximise the information that
they gain on inputs of targets once the computation output is known,
whereas the targets wish to protect the privacy of their inputs.
to compute the output of a public function of their private inputs whilst
protecting the confidentiality of their inputs. But such an output, as a
function of its inputs, inevitably leaks some information about input val-
ues regardless of the protocol used to compute it. We introduce founda-
tions for quantifying and understanding how such leakage may influence
input behaviour of deceitful protocol participants as well as that of par-
ticipants they target. Our model captures the beliefs and knowledge that
participants have about what input values other participants may choose.
In this model, measures of information flow that may arise between pro-
tocol participants are introduced, formally investigated, and experimen-
tally evaluated. These information-theoretic measures not only suggest
advantageous input behaviour to deceitful participants for optimal up-
dates of their beliefs about chosen inputs of targeted participants. They
also allow targets to quantify the information-flow risk of their input
choices. We show that this approach supports a game-theoretic formula-
tion in which deceitful attackers wish to maximise the information that
they gain on inputs of targets once the computation output is known,
whereas the targets wish to protect the privacy of their inputs.
Date Issued
2017-03-28
Date Acceptance
2016-12-22
Citation
Lecture Notes in Computer Science, 2017, 10204, pp.71-92
ISSN
0302-9743
Publisher
Springer Verlag
Start Page
71
End Page
92
Journal / Book Title
Lecture Notes in Computer Science
Volume
10204
Copyright Statement
© Springer-Verlag GmbH Germany 2017. he final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-662-54455-6_4
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/N023242/1
EP/N020030/1
EP/K503381/1
Source
6th International Conference on Principles of Security and Trust (POST)
Subjects
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2017-04-22
Finish Date
2017-04-30
Coverage Spatial
Uppsala, Sweden
Date Publish Online
2017-03-28